Kevin's Take
Stop Using AI to Do the Same Stuff Faster
Everyone's racing to use AI for efficiency. Automate the email sequences. Speed up the content production. Cut the busy work. Fine. That's table stakes now.
But here's what I'm seeing from the CMOs actually moving the needle in 2026: they're not just saving time — they're reinvesting that time into things that didn't exist before. The three hours you save on campaign reporting? That could become a new market experiment. The week your content team gains from AI-assisted production? That's time to build a completely new thought leadership vertical you've been putting off for two years.
Most companies are using what I call "efficiency AI" — tools that make existing processes faster or cheaper. And look, I'm not saying that's bad. If you can cut your content production time by 40 percent, do it. But if you stop there, you're missing the entire point.
The Real Opportunity
The companies pulling ahead right now are using efficiency AI to create what I'm calling "opportunity AI" — the space and resources to do things that generate new revenue, open new markets, or fundamentally change how they compete.
I watched a client cut their campaign planning cycle from six weeks to ten days using AI-assisted research and content generation. Great. But the breakthrough happened when they used those extra weeks to launch a completely new vertical they'd been "too busy" to pursue. That vertical is now 18 percent of their pipeline.
Another CMO used the time saved from AI-automated reporting to finally build the customer advisory board program she'd been pitching for three years. That program has generated more product innovation input in four months than the previous two years of surveys and feedback forms combined.
What This Means for Your Planning
If your AI strategy is just a list of tools that make current work faster, you're thinking too small. The question isn't "What can we automate?" The question is "What becomes possible when we automate this?"
Here's what I'm asking every CMO I work with: For every hour AI saves your team, what are you building that you couldn't build before? New market research? A podcast series your CEO has been asking about? An account-based program for that vertical you've been ignoring? A completely new positioning that requires deep customer interviews you never had time for?
The Signal
AI Dominates B2B Marketing Planning But Implementation Gaps Are Real — Demand Gen Report
Over 300 B2B marketers weighed in on their 2026 priorities, and the data is clear: AI isn't hype anymore, it's infrastructure. The report shows AI dominating marketing conversations and budget allocation discussions, with concrete insights on how marketers are actually deploying it versus how they're planning to.
Why it matters:If you're building your 2026 H2 plans right now, this gives you peer benchmark data to either validate your AI investments or explain why you're not making them. The gap between AI enthusiasm and actual execution is showing up in the data, which means there's still time to be an early-ish adopter without being a guinea pig.
The Pipeline Mandate Is Here And It's Not Going Away — Energize Marketing
A survey of 300 senior marketing leaders confirms what you're probably feeling: executive scrutiny on pipeline contribution has intensified dramatically. Marketing teams are being held to revenue accountability standards that were previously reserved for sales, and buying committees growing more complex isn't helping.
Why it matters:This isn't a temporary pressure spike. The data shows this is a permanent shift in how boards and CEOs evaluate marketing effectiveness. If your attribution model still can't connect marketing activity to closed revenue, that's not a nice-to-have problem anymore. It's a job security problem.
Demandbase Labs Releases GTM Benchmark Data, Finally — Demandbase
Demandbase's new Labs division just published original research benchmarking GTM execution and AI adoption across B2B companies. Instead of vendor-sponsored fluff, this appears to be actual performance data on what separates high-performing GTM teams from everyone else.
Why it matters: Good benchmark data for mid-market tech GTM is rare. If you're trying to figure out whether your sales and marketing integration is actually behind or just feels behind, or whether your AI adoption pace is reasonable, this gives you something better than gut feel to work with.
On Our Radar
AI agents are becoming core GTM infrastructure, but most companies are treating their experience design as an afterthought. If you're deploying agents for lead qualification or customer support, this reframes how to think about their interface and effectiveness.
Supermetrics released their 2026 Marketing Data Report with insights on martech stack optimization and analytics priorities. Particularly useful if you're in the middle of annual planning and trying to figure out which data infrastructure investments actually matter.
OpenAI is investing to build internal ad tech capabilities instead of using outside vendors. That's not a pilot. That's a signal they're planning to compete directly in ad tech, which means your current vendors might have a new existential threat.
FROM THE TRENCHES
The best CMOs in 2026 aren't just marketers anymore — they're builders.
We're seeing a fundamental shift in what boards and CEOs expect from marketing leaders. It's no longer enough to manage agencies, optimize campaigns, and report on metrics. The CMOs getting promoted and retained are the ones who can build systems, develop new capabilities, and switch between strategic models when market conditions change.
The Fix:
We've started coaching our CMO clients on what we call "builder skills" — the ability to construct marketing systems from scratch rather than just buying and implementing platforms. This means understanding when to build versus buy, how to leverage AI to create net-new capabilities rather than just efficiency, and when to completely switch your GTM model instead of optimizing the one you have.
The Result:
The CMOs who embrace this builder mindset are creating asymmetric advantages. They're not waiting for vendors to solve their problems. They're using efficiency gains from AI to fund entirely new market experiments. They're building proprietary systems that become competitive moats.
And they're showing up to board meetings with growth initiatives, not just performance reports.